Computer vision and machine vision are overlapping technologies. A machine vision system requires a computer and specific software to operate while computer vision doesn’t need to be integrated with a machine. Computer vision can, for example, analyze digital online images or videos as well as “images” from motion detectors, infrared sensors or other sources, not just a photo or video. Machine vision is a sub-category of computer vision.
The very core of every machine vision application is the software the performs the actual processing and analysis of the image. At this point specific software tools (“algorithms,” “operators,” etc. depending on the terminology used by the application or library vendor) are configured or programmed to perform specific analysis on the pixel-based data in the acquired image.
In terms of practical implementation, one construct for machine vision software can be described as an application that “configures” the system components and how they execute machine vision functions and tasks. These apps tend to have graphical user interfaces (GUI) devoted to “ease of use” with intuitive and graphically manipulated application configuration steps.
The successful application of machine vision technology involves an intricately and carefully balanced mix of a variety of elements. While the hardware components that perform the tasks of image formation, acquisition, component control, and interfacing are decidedly critical to the solution, machine vision software is the engine “under the hood” that supports and drives the imaging, processing, and ultimately the results.
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Today, designers are converting the image feeds from these sensors into GigE Vision to use traditional machine vision processing for analysis. Looking ahead, there will be obvious value in fully integrating the output from all of the sensors within an application to provide a complete data set for analysis and eventually AI.
Software techniques enable the design of virtual GigE Vision sensors that can be networked to share data with other devices and local or cloud-based processing.
Embedded smart devices enable more sophisticated processing at the sensor level. Key to this has been the introduction of lower-cost, compact embedded boards with processing power required for
Embedded smart devices integrate off-the-shelf sensors and processing platforms to enable compact, lower-power devices that can be more easily networked in IoT applications
Traditionally, inspection has relied on a camera or sensor transmitting data back to a central processor for analysis.
The introduction of the GigE Vision standard in 2006 brought new levels of product interoperability and networking connectivity for machine vision system designers, paving the way for the emergence of IoT.
One of the most hyped technologies in recent years has been the Internet of Things (IoT), a trend that has entered our consumer lives via home monitoring systems, wearable devices, connected cars, and remote health care.